File size: 11,900 Bytes
2948983 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | """최종 selector의 overmerge를 이웃 truth·OCR family·Tray penalty 기준으로 분해한다."""
from __future__ import annotations
import argparse
from collections import Counter
from datetime import datetime, timezone
import json
from pathlib import Path
import sys
from typing import Any
PROJECT_ROOT = Path(__file__).parents[1]
SOURCE_ROOT = PROJECT_ROOT / "src"
for path in (PROJECT_ROOT, SOURCE_ROOT):
if str(path) not in sys.path:
sys.path.insert(0, str(path))
from math_grid_drawer.research.cross_visual import CrossVisualModel
from math_grid_drawer.research.equality_visual import EqualityVisualModel
from math_grid_drawer.research.segmentation_lattice import (
LATTICE_FEATURE_NAMES,
select_lattice_partition,
)
from scripts.crohme_lattice_common import load_cached_split, writer_fit_validation
from scripts.evaluate_crohme_gt_free_grouping import _truth_partition
from scripts.evaluate_crohme_lattice_ocr_fusion import _fit_geometry
from scripts.evaluate_crohme_structure_presence import _truth_structures
from scripts.evaluate_crohme_tray_joint_selector import _prepared_signals, _weighted
def _parse_args() -> argparse.Namespace:
"""필요 변수: 공식 test·cache·full selector head. 작동 원리: 최종 overmerge 감사 CLI를 만든다."""
parser = argparse.ArgumentParser(description="Audit Math Ink 0.6 local-baseline overmerge")
parser.add_argument(
"--train-root", type=Path,
default=PROJECT_ROOT / "research/data/R_noncommercial/ICFHR_package/CROHME2012_data/trainData",
)
parser.add_argument(
"--test-root", type=Path,
default=PROJECT_ROOT / "research/data/R_noncommercial/ICFHR_package/CROHME2012_data/testDataGT",
)
parser.add_argument(
"--cache-dir", type=Path,
default=PROJECT_ROOT / "research/runs/crohme_lattice_ocr_cache_v2_20260722",
)
parser.add_argument(
"--bundle", type=Path,
default=Path(r"research\runs\aiflow_ocr_05_dual_trajectory_3seed_20260720\bundle.manifest.json"),
)
parser.add_argument(
"--cross-model", type=Path,
default=PROJECT_ROOT / "research/runs/crohme_cross_visual_loop3_polyline_20260722/cross_visual.json",
)
parser.add_argument(
"--equality-model", type=Path,
default=PROJECT_ROOT / "research/runs/crohme_equality_visual_loop1_20260722/equality_visual.json",
)
parser.add_argument("--profile", default="median_height_32")
parser.add_argument("--output", type=Path, required=True)
return parser.parse_args()
def main() -> None:
"""필요 변수: gap40·family6 보호 selector. 작동 원리: overmerge candidate와 침범 truth를 1:1 연결한다."""
args = _parse_args()
fit, _validation = writer_fit_validation(args.train_root, args.profile)
geometry_model = _fit_geometry(fit)
equality_model = EqualityVisualModel.load(args.equality_model)
cross_model = CrossVisualModel.load(args.cross_model)
samples, cached = load_cached_split(
args.test_root,
args.cache_dir,
split="official_test",
profile=args.profile,
bundle=args.bundle,
version=2,
)
prepared = _prepared_signals(
samples,
cached,
geometry_model,
equality_model=equality_model,
cross_model=cross_model,
cross_gap_ratio=0.40,
multistroke_family_boost=6.0,
)
weighted = _weighted(
prepared,
tray_weight=4.0,
symbol_weight=4.0,
fraction_weight=8.0,
infix_weight=8.0,
)
path_by_id = {path.stem: path for path in sorted(args.test_root.rglob("*.inkml"))}
truth_labels: Counter[str] = Counter()
candidate_labels: Counter[str] = Counter()
candidate_families: Counter[str] = Counter()
invaded_pairs: Counter[str] = Counter()
structure_counts: Counter[str] = Counter()
fraction_penalty_counts: Counter[str] = Counter()
local_penalty_counts: Counter[str] = Counter()
rows: list[dict[str, Any]] = []
unique_bad_candidates: dict[tuple[str, tuple[int, ...]], dict[str, Any]] = {}
correct_multistroke_rows: list[dict[str, Any]] = []
for sample, row in zip(samples, weighted, strict=True):
truth_groups, labels = _truth_partition(sample, "aiflow_geometry")
label_by_group = dict(zip(truth_groups, (str(value) for value in labels), strict=True))
predicted = set(select_lattice_partition(
row["candidates"], row["logits"], row["stroke_count"], group_bias=-2.0,
))
candidate_index = {
frozenset(int(value) for value in candidate["source_indices"]): index
for index, candidate in enumerate(row["candidates"])
}
families = row.get("ocr_families") or [""] * len(row["candidates"])
structures = _truth_structures(path_by_id[sample["sample_id"]])
for truth, truth_label in label_by_group.items():
if truth in predicted:
if len(truth) > 1:
index = candidate_index[truth]
correct_multistroke_rows.append({
"sample_id": sample["sample_id"],
"truth_label": truth_label,
"truth_group": sorted(truth),
"candidate_label": str(row["ocr_labels"][index]),
"candidate_family": str(families[index]),
"features": {
"ocr_top1": float(
row["features"][index][len(LATTICE_FEATURE_NAMES)]
),
"merge_top1_gain": float(
row["features"][index][len(LATTICE_FEATURE_NAMES) + 6]
),
"merge_entropy_gain": float(
row["features"][index][len(LATTICE_FEATURE_NAMES) + 7]
),
"pair_gap_max": float(row["features"][index][12]),
},
})
continue
overmerged = [
group for group in predicted
if group & truth and bool(group - truth)
]
for group in overmerged:
index = candidate_index[group]
candidate_label = str(row["ocr_labels"][index])
candidate_family = str(families[index])
invaded = [
other_label
for other_group, other_label in label_by_group.items()
if other_group != truth and other_group & group
]
truth_labels[truth_label] += 1
candidate_labels[candidate_label] += 1
candidate_families[candidate_family] += 1
for other_label in invaded:
invaded_pairs[f"{truth_label} -> {other_label}"] += 1
for structure in structures or {"plain"}:
structure_counts[structure] += 1
fraction_penalty = float(row["fraction_penalty"][index])
fraction_penalty_counts[
"nonzero" if fraction_penalty > 0.0 else "zero"
] += 1
raw_local_penalty = float(row["raw_local_baseline_penalty"][index])
local_penalty = float(row["local_baseline_penalty"][index])
if local_penalty > 0.0:
local_penalty_counts["effective_nonzero"] += 1
elif raw_local_penalty > 0.0:
local_penalty_counts["protected_by_positive_signal"] += 1
else:
local_penalty_counts["not_detected"] += 1
covered_truth = [
other_group for other_group in truth_groups if other_group & group
]
replacement_scores = [
float(row["logits"][candidate_index[other_group]])
for other_group in covered_truth if other_group in candidate_index
]
oracle_margin = (
float(row["logits"][index]) - sum(replacement_scores)
+ 2.0 * (len(replacement_scores) - 1)
if len(replacement_scores) == len(covered_truth) else None
)
detail = {
"sample_id": sample["sample_id"],
"structures": sorted(structures),
"truth_label": truth_label,
"truth_group": sorted(truth),
"candidate_group": sorted(group),
"candidate_label": candidate_label,
"candidate_family": candidate_family,
"invaded_truth_labels": invaded,
"fraction_penalty": fraction_penalty,
"raw_local_baseline_penalty": raw_local_penalty,
"local_baseline_penalty": local_penalty,
"oracle_truth_partition_margin": oracle_margin,
"tray_signal": float(row["tray_signal"][index]),
"symbol_signal": float(row["symbol_signal"][index]),
"infix_signal": float(row["infix_signal"][index]),
"score": float(row["logits"][index]),
"geometry": {
"width_ref": float(row["features"][index][2]),
"height_ref": float(row["features"][index][3]),
"aspect_log": float(row["features"][index][4]),
"temporal_span": float(row["features"][index][5]),
"pair_gap_max": float(row["features"][index][12]),
},
"ocr_features": {
"ocr_top1": float(
row["features"][index][len(LATTICE_FEATURE_NAMES)]
),
"merge_top1_gain": float(
row["features"][index][len(LATTICE_FEATURE_NAMES) + 6]
),
"merge_entropy_gain": float(
row["features"][index][len(LATTICE_FEATURE_NAMES) + 7]
),
},
}
rows.append(detail)
unique_bad_candidates[(sample["sample_id"], tuple(sorted(group)))] = detail
report = {
"experiment": "R-MATH-INK-06-LOCAL-BASELINE-OVERMERGE-AUDIT-001",
"generated_at": datetime.now(timezone.utc).isoformat(),
"configuration": {
"cross_gap_ratio": 0.40,
"multistroke_family_boost": 6.0,
},
"overmerge_events": len(rows),
"truth_labels": truth_labels.most_common(),
"candidate_labels": candidate_labels.most_common(),
"candidate_families": candidate_families.most_common(),
"invaded_pairs": invaded_pairs.most_common(),
"structures": structure_counts.most_common(),
"fraction_penalty": dict(fraction_penalty_counts),
"local_baseline_penalty": dict(local_penalty_counts),
"unique_bad_candidate_count": len(unique_bad_candidates),
"unique_bad_candidates": list(unique_bad_candidates.values()),
"correct_multistroke_rows": correct_multistroke_rows,
"rows": rows,
"track": "R_noncommercial_only",
"product_validation": False,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(
json.dumps(report, ensure_ascii=False, indent=2) + "\n",
encoding="utf-8",
)
print(json.dumps({
key: value for key, value in report.items()
if key not in {"rows", "unique_bad_candidates", "correct_multistroke_rows"}
}, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
|